Principal Agentic Architect

Innova Solutions•Charlotte, NC

14h•Hybrid

About The Position

Innova Solutions is immediately hiring for an AI Solutions Architect on a contract basis for 6-12 months. This is a hybrid role, requiring 3 days onsite in Charlotte, NC, and 2 days remote per week. The AI Solutions Architect will lead the design and implementation of a consolidated enterprise data platform, integrating multiple legacy systems into a unified, governed data ecosystem. This includes assessing current systems, defining strategies for master data management, metadata management, data quality, and data lifecycle governance, and establishing a scalable architecture for various data types. The role also involves architecting solutions for systems integration and modernization using modern cloud-native services, defining approaches for data harmonization and transformation, and managing technical dependencies. A key responsibility is establishing authorized data sources and a trusted 'system of record'. Furthermore, the architect will develop semantic AI and knowledge layers, including knowledge graphs and ontologies, to support advanced AI capabilities like RAG and semantic search. Finally, the role requires defining frameworks for deploying AI agents, designing secure orchestration layers, and enabling conversational interfaces and intelligent automation, while establishing governance and monitoring for autonomous AI operations. The position also involves leadership, strategy development, and mentoring junior team members.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or related field.
Experience in enterprise data architecture, systems architecture, or digital transformation initiatives.
Proven experience consolidating complex legacy systems into modern enterprise platforms.
Strong expertise in: Data architecture and modeling
Enterprise integration architecture
Cloud platforms (Azure, AWS, or Google Cloud)
Data governance and master data management
Metadata management and data catalogs
API and event-driven architectures
Large-scale data migrations and modernization programs
Experience designing enterprise semantic layers, knowledge graphs, or ontology-driven architectures.
Deep understanding of AI, machine learning, large language models (LLMs), and retrieval architectures.
Experience leading enterprise-wide transformation programs involving multiple stakeholders and business units.

Responsibilities

Lead the design and implementation of a consolidated enterprise data platform that integrates multiple legacy systems into a unified, governed data ecosystem.
Assess current-state systems, data models, integrations, and business processes to develop target-state architecture.
Define strategies for master data management (MDM), metadata management, data quality, and data lifecycle governance.
Establish a scalable architecture supporting structured, semi-structured, and unstructured enterprise data.
Architect solutions leveraging APIs, event-driven architectures, data fabrics, data lakes, and modern cloud-native services.
Define approaches for data harmonization, transformation, deduplication, and lineage tracking.
Manage technical dependencies and integration risks across multiple business domains.
Establish and maintain a trusted "system of record" and enterprise-wide single source of truth.
Implement governance frameworks ensuring data consistency, ownership, security, compliance, and auditability.
Partner with business stakeholders to define authoritative data domains and stewardship models.
Create enterprise taxonomy and business-aligned data standards.
Design and implement a semantic layer that provides business context and meaning across enterprise data assets.
Develop knowledge graph, ontology, and metadata strategies that support advanced AI and search capabilities.
Create architectures that enable retrieval-augmented generation (RAG), semantic search, and contextual reasoning.
Ensure data structures and governance practices support AI-ready enterprise information.
Define frameworks for deploying AI agents capable of interacting with enterprise systems and workflows.
Design secure agent orchestration layers leveraging governed enterprise data and business logic.
Enable conversational interfaces, intelligent automation, task execution, and decision-support capabilities.
Establish guardrails, monitoring, observability, and governance for autonomous AI operations.
Serve as the technical leader for enterprise modernization initiatives.
Develop multi-year architecture roadmaps aligned with organizational business objectives.
Mentor data engineers, architects, AI specialists, and platform teams.
Present solution designs and recommendations to executive leadership and business stakeholders.